



'''simple docstring'''

import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME






def 				a  (     UpperCamelCase_    :    Union[str, Any]			)       ->	Any:
		snake_case__	     =[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase_      ,					UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    List[Any]			)       ->	Optional[int]:
		snake_case__     ,	snake_case__	     =emb.weight.shape
		snake_case__	     =nn.Linear(UpperCamelCase_      ,					UpperCamelCase_      ,					bias=UpperCamelCase_			)
		snake_case__	     =emb.weight.data
		return lin_layer






def 				a  (     UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    Dict=None			)       ->	Tuple:
		snake_case__	     ={}
		for old_key in state_dict.keys():
				snake_case__	     =old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								snake_case__	     =key.replace('moe_layer.experts.0'      ,					f"""ffn.experts.expert_{expert_idx}"""			)
						else:
								snake_case__	     =key.replace('moe_layer.experts.'      ,					'ffn.experts.expert_'			)
				if "gate" in key:
						snake_case__	     =key.replace('.moe_layer.gate.wg'      ,					'.ffn.router.classifier'			)
				if "fc2" and "experts" not in key:
						snake_case__	     =key.replace('.fc2.'      ,					'.ffn.fc2.'			)
				if "fc1" and "experts" not in key:
						snake_case__	     =key.replace('.fc1.'      ,					'.ffn.fc1.'			)
				if ".encoder_attn." in key:
						snake_case__	     =key.replace('.encoder_attn.'      ,					'.cross_attention.'			)
				if "encoder_attn_layer_norm" in key:
						snake_case__	     =key.replace('encoder_attn_layer_norm'      ,					'cross_attention_layer_norm'			)
				if "final_layer_norm" in key:
						snake_case__	     =key.replace('final_layer_norm'      ,					'ff_layer_norm'			)
				snake_case__	     =state_dict[old_key]
		return new_dict






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    str = WEIGHTS_NAME			)       ->	List[str]:
		snake_case__	     =[]
		snake_case__	     =0
		os.makedirs(UpperCamelCase_      ,					exist_ok=UpperCamelCase_			)

		for expert in range(UpperCamelCase_			):
				snake_case__	     =switch_checkpoint_path + f"""-rank-{expert}.pt"""
				if os.path.isfile(UpperCamelCase_			):
						snake_case__	     =torch.load(UpperCamelCase_			)['model']
						remove_ignore_keys_(UpperCamelCase_			)
						snake_case__	     =rename_fairseq_keys(UpperCamelCase_      ,					UpperCamelCase_			)
						snake_case__	     =os.path.join(
						    UpperCamelCase_      ,					weights_name.replace('.bin'      ,					f"""-{len(UpperCamelCase_			)+1:05d}-of-???.bin"""			)			)
						torch.save(UpperCamelCase_      ,					UpperCamelCase_			)
						sharded_state_dicts.append(expert_state.keys()			)
						total_size += sum([value.numel() for key, value in expert_state.items()]			) * dtype_byte_size(
						    expert_state[list(UpperCamelCase_			)[0]].dtype			)

    # Add the last block
		snake_case__	     =os.path.join(UpperCamelCase_      ,					weights_name.replace('.bin'      ,					f"""-{len(UpperCamelCase_			)+1:05d}-of-???.bin"""			)			)
		snake_case__	     =torch.load(switch_checkpoint_path + '-shared.pt'			)['model']
		remove_ignore_keys_(UpperCamelCase_			)
		snake_case__	     =rename_fairseq_keys(UpperCamelCase_      ,					UpperCamelCase_			)
		snake_case__	     =shared_weights['decoder.embed_tokens.weight']
		sharded_state_dicts.append(shared_weights.keys()			)

		# If we only have the shared weights (dummy model/experts saved on the same file)
		if len(UpperCamelCase_			) == 1:
				snake_case__	     =os.path.join(UpperCamelCase_      ,					UpperCamelCase_			)
				torch.save(UpperCamelCase_      ,					UpperCamelCase_			)
				return {weights_name: sharded_state_dicts[0]}, None
		else:
				torch.save(UpperCamelCase_      ,					UpperCamelCase_			)
		# Otherwise, let's build the index
		snake_case__	     ={}
		for idx, shard in enumerate(UpperCamelCase_			):
				snake_case__	     =weights_name.replace('.bin'      ,					f"""-{idx+1:05d}-of-{len(UpperCamelCase_			):05d}.bin"""			)
				snake_case__	     =os.path.join(UpperCamelCase_      ,					weights_name.replace('.bin'      ,					f"""-{idx+1:05d}-of-???.bin"""			)			)
				os.rename(UpperCamelCase_      ,					os.path.join(UpperCamelCase_      ,					UpperCamelCase_			)			)
				for key in shard:
						snake_case__	     =shard_file

    # Add the metadata
		snake_case__	     ={'total_size': total_size}
		snake_case__	     ={'metadata': metadata, 'weight_map': weight_map}

		with open(os.path.join(UpperCamelCase_      ,					UpperCamelCase_			)      ,					'w'      ,					encoding='utf-8'			) as f:
				snake_case__	     =json.dumps(UpperCamelCase_      ,					indent=2      ,					sort_keys=UpperCamelCase_			) + '\n'
				f.write(UpperCamelCase_			)

		return metadata, index


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--nllb_moe_checkpoint_path''',
			    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
			    type=str,
			    required=False,
			    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
			)
			parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
			    type=str,
			    required=False,
			    help='''Path to the output pytorch model.''',
			)
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  parser.parse_args()
			SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__     :						List[Any]	=  shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    1_2_8,
			    args.dtype,
			)

			SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  NllbMoeConfig.from_pretrained(
			    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
			)
			config.save_pretrained(args.pytorch_dump_folder_path)
			SCREAMING_SNAKE_CASE__     :						Tuple	=  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('''Done''')
			model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''

from timeit import timeit






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				number &= number - 1
				result += 1
		return result






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result






def 				a  (     )       ->	None:

		def do_benchmark(UpperCamelCase_    :    int			) -> None:
				snake_case__	     ='import __main__ as z'
				print(f"""Benchmark when {number = }:"""			)
				print(f"""{get_set_bits_count_using_modulo_operator(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit('z.get_set_bits_count_using_modulo_operator(25)'      ,					setup=UpperCamelCase_			)
				print(f"""timeit() runs in {timing} seconds"""			)
				print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit(
				    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'      ,					setup=UpperCamelCase_      ,					)
				print(f"""timeit() runs in {timing} seconds"""			)

		for number in (25, 37, 58, 0):
				do_benchmark(UpperCamelCase_			)
				print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()




